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aby0

reading-companion

by aby0

save_profile

Save a reader's profile after an interview, including reading domains, goals, preferences, and current context, to establish personalized reading plans.

Instructions

Save the user's reading profile after the interview.

Args: name: User's name

domains: List of reading domains with goals. Each domain should have:
    - id: Short identifier (e.g., "classic_lit", "neuroscience")
    - name: Display name (e.g., "Classic Literature")
    - purpose: Why they want to read in this area
    - target_books: Number of books they aim to read

preferences: Reading preferences dictionary:
    - pacing: "slow_deep" | "steady" | "fast_volume"
    - challenge_tolerance: "low" | "medium" | "high"
    - parallel_books: Number of books they read at once

context: Current context dictionary:
    - mood: Current reading mood
    - avoidances: List of things to avoid

Returns: Confirmation with status and message

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
domainsYes
preferencesYes
contextYes
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must disclose behavioral traits. It clearly indicates a write operation (save), and details parameter structure, but does not mention side effects (e.g., overwriting existing profile), required permissions, or success/failure conditions. The return value is briefly noted as a confirmation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is fairly long but well-structured with clear sections for each parameter. However, it is not concise; some explanations could be shortened without losing clarity. The front-loading of the purpose is good, but the parameter details could be more compact.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 4 required parameters, nested objects, and no output schema, the description covers the input structure well and mentions return value. It does not explain error handling or partial saves, but given the complexity, it is reasonably complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Given 0% schema coverage, the description fully compensates by explaining each parameter's structure, including sub-fields for domains, preferences, and context. It provides meaningful detail beyond the schema, such as example values and the purpose of each field.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool saves the user's reading profile and specifies it is used after an interview. The verb 'save' is specific and distinguishes from siblings like 'get_profile' (retrieval) and 'start_interview' (initiation), though it does not explicitly contrast all siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implicitly suggests use after an interview via the phrase 'after the interview', but does not explicitly state when to use vs alternatives, nor provide any when-not-to-use guidance. More explicit context about its role in the reading profile pipeline would help.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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